Biology subjects

Martin, L. K.

Publications and source records attributed to Martin, L. K..

3 recordsLinked to original sources

bioRxiv2026

The Virtual Child Brain: Modeling Neuromaturational Trajectories

IntroductionUnderstanding the mechanisms of human neuromaturation constitutes one of the fundamental questions of neuroscience. While it is well described that large-scale brain maturation is initiated within sensorimotor brain regions and progresses to associative cortex, the underlying developmental neurobiology remains to be fully characterized. Animal models have indicated that cortical inhibitory upregulation might be a driver of neurodevelopment. To investigate the hypothesis that cortical inhibitory upregulation plays a similar role in human neuromaturation, we developed a The Virtual Brain (TVB) based computational model (TVB-Child) to explore potential mechanisms of human neurodevelopment. Material and methodWe created neurodevelopmental dynamic brain network models capturing neurobiological maturation by using the large-scale brain simulator TVB and fitting brain network models to developmental functional MRI (fMRI) from the Human Connectome Project-Development (HCP-D) data set with 640 subjects with an age range of 6-21 years. Age-dependent trajectories in the fMRI data set were first analyzed by combined group-ICA/Dual Regression extracting subject-specific resting-state networks (RSN). Maturational topographical and topological redistribution of these networks were analyzed by linear and non-linear regression of RSN size and degree and strength centrality. Brain network models were fitted to the fMRI functional connectivity obtained from the HCP-D data set. Hypothesizing that cortical inhibition is a driver of neuromaturation, we analyzed spatiotemporal inhibition parameter gradients in the dynamic brain network model for the hypothesized significant correlations with fMRI RSN maturational trajectories. ResultsWhile during development frontoparietal (FP) and default mode network (DMN) grew and exhibited an increase in both degree and strength centrality, becoming dominant network hubs, the attention network underwent network pruning with a decrease in size and node degree. The primary sensory network changed little. For the fitted brain network models, we obtained a high degree of reproduction with correlation coefficients between empirical and simulated functional connectivities ranging between 0.80 and 0.95. Values of the feed forward inhibition model parameter [Formula] representing the strength of regional feedforward inhibitory input exhibited the most significant increase with age within the FP and DMN networks. A less pronounced, but significant, age-dependent increase of the inhibitory parameter values were seen in attention networks and no change within primary sensory networks. ConclusionOur study shows that high order (FP, DMN), attention and primary sensory networks exhibit distinct topographical and topological maturation trajectories. Moreover, brain network modeling revealed RSN-specific age-dependent inhibition trajectories, indicating that the model is able to reproduce and thus support candidate mechanisms of neurodevelopment. Graphical abstractOverview of the study combining empirical and modeling analyses of neuromaturation in 640 study participants from the HCP-D data set, age range 6-21 years old. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=149 SRC="FIGDIR/small/737052v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@1764432org.highwire.dtl.DTLVardef@17740ceorg.highwire.dtl.DTLVardef@3fa9a6org.highwire.dtl.DTLVardef@19afc80_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2026

Brain network modeling with The Virtual Brain derives pharmacodynamics of ketamine

Ketamine, an N-Methyl-D-aspartate receptor (NMDAR) antagonist, is used clinically as an anesthetic and antidepressant, and is also known for its psychotomimetic effects. Its impact on brain dynamics and behavior varies significantly with dosage likely via a dose-dependent modulation of the NMDARergic transmission. Currently, it is unclear how molecular changes at the microscopic level of NMDAR antagonism lead to large-scale changes in brain dynamics. We implement a dose-dependent NMDAR antagonism based on ketamines disinhibition theory into a biophysically grounded mean-field model within The Virtual Brain (TVB) framework to replicate ketamines key signatures across its dose spectrum. Our results imply that in low doses ketamine preferentially impairs excito-inhibitory neurotransmission while in higher doses antagonism on excito-excitatory connections plays a role. These findings highlight the utility of computational modeling for disentangling dose-specific mechanisms of action and provide a framework for exploring NMDAR-related interventions. Author summaryKetamine is a dissociative anesthetic at high doses, but at lower, sub-anesthetic doses, it has garnered significant interest for its rapid-acting antidepressant and anxiolytic effects. Despite its growing clinical use in psychiatric conditions, the precise neural mechanisms underlying ketamines dose-dependent effects remain incompletely understood. Ketamine primarily acts as a non-competitive antagonist of the NMDAR, which is expressed on both excitatory and inhibitory neurons throughout the cortex. One of the leading hypotheses explaining its antidepressant effects is the disinhibition theory which proposes that low doses of ketamine preferentially block NMDARs on inhibitory interneurons, resulting in increased cortical excitability. At high doses ketamine exerts anesthetic effects potentially through more widespread NMDAR antagonism including on excitatory neurons. In this study, we used a computational model to explore how selective NMDAR antagonism at different doses affects large-scale brain dynamics. A key novelty of our work is the integration of ketamines full dose spectrum within a single computational modeling framework, allowing us to relate distinct neural effects from disinhibition to anesthesia to experimental findings. This modeling approach contributes to a deeper understanding of how ketamine modulates cortical activity across different contexts.

neuroscience↗

bioRxiv2023

Modelling the dynamics of senescence spread

Cellular senescence is a cell surveillance mechanism that arrests the cell cycle in damaged cells. The senescent phenotype can spread from cell to cell through paracrine and juxtacrine signalling, but the dynamics of this process are not well understood. Although senescent cells are important in ageing, wound healing, and cancer, it is unclear how the spread of senescence is contained in senescent lesions. In the absence of the immune system, senescence could theoretically spread infinitely from one cell to another, but this contradicts experimental evidence. To investigate this issue, we developed both a minimal mathematical model and a stochastic simulation of senescence spread. Our results suggest that differences in the number of signalling molecules secreted between subtypes of senescent cells can limit the spread of senescence. We found that dynamic, timedependent paracrine signalling prevents the uncontrolled spread of senescence and we demonstrate how model parameters can be determined using Bayesian inference in a proposed experiment.

systems biology↗